We collected nine saliva samples throughout the day: Five samples were taken in the morning in order to assess the Cortisol Awakening Response (CAR) and four samples were taken in the afternoon in order to assess the Diurnal Baseline Cortisol (DBC) levels. After 8 months, a follow-up measurement was conducted, repeating all the measures as at t1. We previously identified a flattened CAR in patients with functional neurological disorders (FND) in comparison to healthy controls (HC).
The exact procedure of the saliva sampling can be found here: Weber - 2022 - Identification of biopsychologial trait markers in functional neurological disorders
In the previous work, we could show that FND patients have a flatter cortisol awakening response (CAR) compared to healthy controls (HC), Figure 1.
Figure 1
Also, we could show that the reduced CAR was associated not only with severity but also duration of past emotional neglect (Figure 2).
This suggests some sort of long-term maladaptive habituation of the HPA-axis in FND as a response to prolonged emotional stress/neglect!
Figure 2
Additionally, we could show that FND patients had a significantly reduced hippocampal (and amygdalar) volume (Figure 3).
Figure 3
In healthy controls, we found that higher cortisol levels were associated with smaller brain volumes. This would support a neurotoxicity hypothesis. Due to the absence of such an association in FND, we suggest that reduced hippocampal volume potentially represents a biological vulnerability factor for FND in the form of a stress-diathesis model, which refers to having a predisposition to a condition which might be activated through stress.
Based on previous results, we aim at investigating the natural course of FND. As such, 53 FND patients participated in a follow-up measurement. At both timepoints (T1 and follow-up(FUP)), the following measures were done:
Only at t1, we assessed the following data:
In total, 53 FND patients participated in the follow-up measurement. We can first look at demographic and clinical characteristics.
| Demographic and clincal characteristics | ||||
| Total | t1 | fup | P-value | |
|---|---|---|---|---|
| Age, mean (SD) | 38 (±15) | 38 (±15) | 38 (±15) | 1.00 |
| Sex [females] | 76 (72%) | 38 (72%) | 38 (72%) | 1.0 |
| BDI - Depression, mean (SD) | 13 (±10) | 13 (±10) | 12 (±11) | 0.66 |
| STAI1 - State Anxiety, mean (SD) | 36 (±11) | 35 (±11) | 36 (±11) | 0.60 |
| S-FMDRS - Symptom Severity, mean (SD) | 8 (±10) | 8 (±9) | 9 (±11) | 0.65 |
| Subjective Symptom Severity, mean (SD) | 41 (±29) | 43 (±29) | 39 (±29) | 0.45 |
Patients did not differ in their clinical characteristics from T1 to Follow-up. We can also look at the plots and the individual development.
Previously, we analysed two metrics to assess cortisol levels: the cortisol awakening response (CAR) and the diurnal baseline cortisol (DBC). The CAR describes the rapid increase in cortisol secretion across the first 30–45 min upon awakening. To assess cortisol differences between T1 and Follow-up in the CAR, a repeated-measures ANOVA was used on the fitted data of the five morning samples (wake-up until 60 min post-awakening) using a linear mixed model with fixed effects of factor Session (T1 vs. FUP) and time point, and using age, sex, symptom severity, smoking, hormonal contraception, psychotropic medication, and menopause as covariates of no interest. The DBC represents the dynamic changes of cortisol throughout the afternoon (from 2 p.m. to 5 p.m.). As previously, no changes in DBC were found, I did not analyse DBC in the longitudinal data. For the analyses of the CAR, I excluded data from one FND patients it did not properly adhere to the saliva sampling protocol (delayed sampling with a strict sampling accuracy margin of Δt > 5 min for post-awakening samples).
First we visualize our data.
We can already see that the values are relatively stable (at FUP in comparison to T1). Next we run the statistics.
In our model, we can see that there is no significant effect of Session - no differences in cortisol between T1 and FUP. However, there seems to be a significant effect of symptom severity (S-FMDRS) on the cortisol.
There is a significant association between changes in symptom severity and changes in cortisol. Visually and statistically, we cannot detect any differences between the measurement timepoints, nor any interactione effects between Session (t1 vs. follow-up) and symptom severity (sfmdrs). This means:
So, how exactly are changes in symptom severity associated with the (non-visible) changes in the CAR without a (session) interaction effect?
## Df Sum Sq Mean Sq F value Pr(>F)
## timepoint 4 241.6 60.40 10.718 2.41e-08 ***
## Session 1 0.0 0.00 0.000 0.983292
## sfmdrs 1 79.2 79.24 14.060 0.000198 ***
## DurationSymptoms 1 28.5 28.47 5.051 0.025036 *
## gender 1 31.3 31.28 5.551 0.018855 *
## smoke 1 0.0 0.00 0.001 0.978562
## psychMed 1 22.6 22.60 4.011 0.045747 *
## menopause 1 1.4 1.44 0.256 0.613001
## contraception 1 11.3 11.25 1.997 0.158213
## age 1 14.1 14.14 2.510 0.113779
## Session:sfmdrs 1 9.1 9.13 1.619 0.203760
## Residuals 505 2845.9 5.64
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
In order to reduce the dimensionality of our data, we can calculate delta scores, i.e., subtracting t1 from fup data. This will reduce our model by one full factor (Session). We again plot our data, and run a repeated-measures ANOVA was used on the fitted data of the five morning samples (wake-up until 60 min post-awakening) using a linear mixed model with fixed effects of time point, and using age, sex, symptom severity, smoking, hormonal contraception, psychotropic medication, and menopause as covariates of no interest. (Caveat: Factor Session is removed, as we work with Delta Scores)
We again detect a significant effect of symptom severity. This confirms: A change in symptom severity is associated with a change in CAR.
## Df Sum Sq Mean Sq F value Pr(>F)
## timepoint 4 18.4 4.60 0.678 0.60801
## deltaSFMDRS 1 50.7 50.70 7.464 0.00676 **
## deltaBDI 1 101.7 101.71 14.975 0.00014 ***
## deltaSTAI 1 0.0 0.02 0.002 0.96045
## gender 1 0.0 0.02 0.003 0.95667
## smoke 1 0.6 0.64 0.095 0.75863
## contraception 2 69.6 34.80 5.124 0.00662 **
## age 1 12.0 12.04 1.773 0.18425
## psychMed 1 36.2 36.15 5.323 0.02190 *
## timepoint:deltaSFMDRS 4 40.3 10.07 1.482 0.20815
## Residuals 242 1643.7 6.79
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
In order to disentangle the effect of symptom severity on the CAR, we stratify our patients into subgroups based on the S-FMDRS score. As such, we created a delta-score by subtracting the t1 score from the fup score. This leaves us with:
We now look at patients who improved in comparison to those who remained or worsened in their symptom severity.
It seems that patients that improved had generally a lower S-FMDRS score as patients that remained or worsened. Using the ANOVA (Chapter 2), we did not detect any differences in symptom severity between time points. Let’s do baseline and FUP comparisons, as well as another ANOVA with another factor (clinical course).
Statistical test We detect a significant effect of group (F(1,950) = 7.51, P = 0.006), as well as a significant interaction effect between group and session (F(1,950) = 25.09, P > 0.0001). This means that:
## Df Sum Sq Mean Sq F value Pr(>F)
## Session 1 188 187.6 1.981 0.15963
## group 1 711 711.1 7.510 0.00625 **
## Session:group 1 2376 2375.6 25.089 6.53e-07 ***
## Residuals 950 89951 94.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Fit: glm(formula = sfmdrs ~ groups + bdi + stai1, family = gaussian)
##
## Linear Hypotheses:
## Estimate
## (worse.fup) - (worse.t1) == 0 3.6436
## (improved.fup) - (improved.t1) == 0 -3.6029
## (worse.fup) - (improved.fup) == 0 6.4895
## (worse.t1) - (improved.t1) == 0 -0.7570
## ((worse.fup) - (worse.t1)) - ((improved.fup) - (improved.t1)) == 0 7.2464
## Std. Error
## (worse.fup) - (worse.t1) == 0 0.7500
## (improved.fup) - (improved.t1) == 0 1.0832
## (worse.fup) - (improved.fup) == 0 0.9478
## (worse.t1) - (improved.t1) == 0 0.9346
## ((worse.fup) - (worse.t1)) - ((improved.fup) - (improved.t1)) == 0 1.3182
## z value
## (worse.fup) - (worse.t1) == 0 4.858
## (improved.fup) - (improved.t1) == 0 -3.326
## (worse.fup) - (improved.fup) == 0 6.847
## (worse.t1) - (improved.t1) == 0 -0.810
## ((worse.fup) - (worse.t1)) - ((improved.fup) - (improved.t1)) == 0 5.497
## Pr(>|z|)
## (worse.fup) - (worse.t1) == 0 1.18e-06 ***
## (improved.fup) - (improved.t1) == 0 0.000881 ***
## (worse.fup) - (improved.fup) == 0 7.55e-12 ***
## (worse.t1) - (improved.t1) == 0 0.417955
## ((worse.fup) - (worse.t1)) - ((improved.fup) - (improved.t1)) == 0 3.86e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- none method)
| Patient characteristics at T1, based on later clinical improvement | ||||
| Total | improved | worsened/remained | P-value | |
|---|---|---|---|---|
| Age, mean (SD) | 38 (±15) | 44 (±18) | 35 (±12) | 0.057 |
| Sex [females] | 38 (72%) | 12 (71%) | 26 (72%) | 1.0 |
| BDI - Depression, mean (SD) | 13 (±10) | 15 (±10) | 12 (±10) | 0.36 |
| STAI1 - State Anxiety, mean (SD) | 35 (±11) | 35 (±10) | 35 (±11) | 0.94 |
| S-FMDRS - Symptom Severity, mean (SD) | 8 (±9) | 9 (±4) | 8 (±11) | 0.57 |
| Subjective Symptom Severity, mean (SD) | 43 (±29) | 50 (±27) | 41 (±30) | 0.30 |
| Patient characteristics based on clinical improvement at FUP | ||||
| Total | improved | worsened/remained | P-value | |
|---|---|---|---|---|
| Age, mean (SD) | 38 (±15) | 44 (±18) | 35 (±12) | 0.057 |
| Sex [females] | 38 (72%) | 12 (71%) | 26 (72%) | 1.0 |
| BDI - Depression, mean (SD) | 12 (±11) | 14 (±9) | 12 (±11) | 0.49 |
| STAI1 - State Anxiety, mean (SD) | 36 (±11) | 34 (±10) | 38 (±11) | 0.26 |
| S-FMDRS - Symptom Severity, mean (SD) | 9 (±11) | 5 (±4) | 11 (±13) | 0.099 |
| Subjective Symptom Severity, mean (SD) | 39 (±29) | 39 (±27) | 39 (±31) | 0.97 |
For completedness, we can also look at differences in BDI and STAI.
Statistical test
## [1] "Results BDI:"
## Df Sum Sq Mean Sq F value Pr(>F)
## Session 1 180 179.7 1.755 0.185615
## group 1 1234 1233.7 12.048 0.000542 ***
## Session:group 1 11 10.7 0.105 0.746315
## Residuals 950 97273 102.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Fit: glm(formula = bdi ~ groups + stai1 + sfmdrs, family = gaussian)
##
## Linear Hypotheses:
## Estimate
## (worse.fup) - (worse.t1) == 0 -2.5409
## (improved.fup) - (improved.t1) == 0 0.2413
## (worse.fup) - (improved.fup) == 0 -5.2781
## (worse.t1) - (improved.t1) == 0 -2.4959
## ((worse.fup) - (worse.t1)) - ((improved.fup) - (improved.t1)) == 0 -2.7822
## Std. Error
## (worse.fup) - (worse.t1) == 0 0.6406
## (improved.fup) - (improved.t1) == 0 0.9268
## (worse.fup) - (improved.fup) == 0 0.8080
## (worse.t1) - (improved.t1) == 0 0.7912
## ((worse.fup) - (worse.t1)) - ((improved.fup) - (improved.t1)) == 0 1.1356
## z value
## (worse.fup) - (worse.t1) == 0 -3.966
## (improved.fup) - (improved.t1) == 0 0.260
## (worse.fup) - (improved.fup) == 0 -6.532
## (worse.t1) - (improved.t1) == 0 -3.155
## ((worse.fup) - (worse.t1)) - ((improved.fup) - (improved.t1)) == 0 -2.450
## Pr(>|z|)
## (worse.fup) - (worse.t1) == 0 7.30e-05 ***
## (improved.fup) - (improved.t1) == 0 0.79458
## (worse.fup) - (improved.fup) == 0 6.48e-11 ***
## (worse.t1) - (improved.t1) == 0 0.00161 **
## ((worse.fup) - (worse.t1)) - ((improved.fup) - (improved.t1)) == 0 0.01428 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- none method)
## [1] "Results STAI-1:"
## Df Sum Sq Mean Sq F value Pr(>F)
## Session 1 286 285.6 2.518 0.11287
## group 1 800 799.8 7.052 0.00805 **
## Session:group 1 611 611.2 5.389 0.02047 *
## Residuals 950 107748 113.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Fit: glm(formula = stai1 ~ groups + bdi + sfmdrs, family = gaussian)
##
## Linear Hypotheses:
## Estimate
## (worse.fup) - (worse.t1) == 0 3.0980
## (improved.fup) - (improved.t1) == 0 -1.0375
## (worse.fup) - (improved.fup) == 0 5.8364
## (worse.t1) - (improved.t1) == 0 1.7008
## ((worse.fup) - (worse.t1)) - ((improved.fup) - (improved.t1)) == 0 4.1356
## Std. Error
## (worse.fup) - (worse.t1) == 0 0.6825
## (improved.fup) - (improved.t1) == 0 0.9894
## (worse.fup) - (improved.fup) == 0 0.8617
## (worse.t1) - (improved.t1) == 0 0.8477
## ((worse.fup) - (worse.t1)) - ((improved.fup) - (improved.t1)) == 0 1.2093
## z value
## (worse.fup) - (worse.t1) == 0 4.539
## (improved.fup) - (improved.t1) == 0 -1.049
## (worse.fup) - (improved.fup) == 0 6.773
## (worse.t1) - (improved.t1) == 0 2.006
## ((worse.fup) - (worse.t1)) - ((improved.fup) - (improved.t1)) == 0 3.420
## Pr(>|z|)
## (worse.fup) - (worse.t1) == 0 5.65e-06 ***
## (improved.fup) - (improved.t1) == 0 0.294349
## (worse.fup) - (improved.fup) == 0 1.26e-11 ***
## (worse.t1) - (improved.t1) == 0 0.044811 *
## ((worse.fup) - (worse.t1)) - ((improved.fup) - (improved.t1)) == 0 0.000627 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- none method)
We can indeed observe a very interesting behaviour.
What does this tell us about the CAR?
Next, we stratify our CAR data based on clinical course after 8 months. We can see that improvement of the symptoms is associated with a temporal shift in the CAR.
We can look again at our delta scores, and we can see: Patients who improved had
Patients who worsened/remained had
And this, ladies and gentlemen, is our “hidden” effect of symptom severity. Why is this particularly interesting? The CAR gets mostly triggered by the hippocampus and it may be linked to the hippocampus’ preparation of the HPA axis in anticipation of metabolic demands and stress.
## [1] "We play around with another threshold for symptom severity:"
##
## improved same/worse same/worsened
## 80 36 0
As a next step, we look at the statistics of the delta scores, and run post-hoc analyses on the individual timepoints to confirm that they significantly differ depending on clinical course. The post-hoc analyses might uncover the interaction, which cannot be found in the previous tests. Indeed: An improvement in symptom severity is associated with with a temporal shift in the CAR.
##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Fit: lm(formula = sfmdrs ~ Cortisol * groups + bdi + contraception +
## age + psychMed, family = gaussian)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## (worse.1) - (improved.1) == 0 7.080 1.251 5.661 4.38e-08 ***
## (worse.2) - (improved.2) == 0 6.588 1.195 5.511 9.34e-08 ***
## (worse.3) - (improved.3) == 0 7.105 1.181 6.014 6.86e-09 ***
## (worse.4) - (improved.4) == 0 7.372 1.184 6.229 2.15e-09 ***
## (worse.5) - (improved.5) == 0 7.231 1.236 5.851 1.63e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- none method)
This analyses was suggested by Fabian. Basically, he said it could be interesting to look at the amplitude of cortisol throughout the day. So we basically calculate the amplitude (height) between the lowest and the highest peak of the cortisol within one day.
## [1] "Amplitude"
Statistical Analyses:
Can the cortisol amplitude predict clinical course (group improved vs. same or worse)? No, it cannot. However, it can predict symptom severity at fup.
## Df Sum Sq Mean Sq F value Pr(>F)
## Session 1 0.1 0.12 0.015 0.90193
## group 1 9.9 9.93 1.292 0.25859
## sfmdrs 1 65.7 65.65 8.545 0.00434 **
## DurationSymptoms 1 4.5 4.47 0.582 0.44733
## gender 1 22.4 22.39 2.914 0.09113 .
## smoke 1 2.7 2.72 0.354 0.55329
## psychMed 1 13.4 13.42 1.747 0.18948
## menopause 1 0.1 0.14 0.019 0.89198
## contraception 1 10.7 10.68 1.390 0.24139
## age 1 4.7 4.71 0.614 0.43543
## Session:group 1 0.4 0.39 0.050 0.82309
## Residuals 94 722.2 7.68
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
We previously identified a reduced hippocampal volume in FND patients, unrelated to the CAR, and suggested this to be a trait vulnerability factor for the disorder. Let’s have a look at brain data.
As a next step, we look into structural alterations between the two timepoints. We first look at the data in spm, where we implemented a regression analysis with symptom severity as dependent variable. Based on a first look in spm, it seems that changes in symptom severity are associated with changes in the
Figure 4
This analyses do not survive when adding BDI as a covariate. To make any further statements on the effect of symptom severity, we have to extract the data for external analyes in R. We extracted ROI-wise (based on AAL3) averaged data and run again a linear model using BDI (and STAI) as a covariate of no-interest, also we stratify directly in groups based on clinical course. I only looked at those ROIs in which we found significant differences associated to SFMDRS (no BDI cov) in spm. There are no significant effects or interactions between groups, timepoint and brain volume. When removing BDI and STAI as covariate, a trend can be found in the Nucleus Caudate (does not survive post-hoc mcp):
## [1] "Results Regression Analysis of Brain Volume in R:"
## Df Sum Sq Mean Sq F value Pr(>F)
## Session 1 0.06 0.0573 0.106 0.7450
## sfmdrs 1 1.69 1.6882 3.134 0.0798 .
## group 1 0.03 0.0287 0.053 0.8178
## Session:sfmdrs 1 0.03 0.0264 0.049 0.8254
## Session:group 1 0.08 0.0821 0.152 0.6970
## sfmdrs:group 1 0.78 0.7817 1.451 0.2312
## Session:sfmdrs:group 1 0.03 0.0302 0.056 0.8133
## Residuals 98 52.78 0.5386
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Fit: glm(formula = Caud ~ groups, family = gaussian)
##
## Linear Hypotheses:
## Estimate
## (worse.fup) - (worse.t1) == 0 0.05817
## (improved.fup) - (improved.t1) == 0 0.02177
## (worse.fup) - (improved.fup) == 0 -0.04058
## (worse.t1) - (improved.t1) == 0 -0.07699
## ((worse.fup) - (worse.t1)) - ((improved.fup) - (improved.t1)) == 0 0.03640
## Std. Error
## (worse.fup) - (worse.t1) == 0 0.17360
## (improved.fup) - (improved.t1) == 0 0.25263
## (worse.fup) - (improved.fup) == 0 0.21675
## (worse.t1) - (improved.t1) == 0 0.21675
## ((worse.fup) - (worse.t1)) - ((improved.fup) - (improved.t1)) == 0 0.30653
## z value
## (worse.fup) - (worse.t1) == 0 0.335
## (improved.fup) - (improved.t1) == 0 0.086
## (worse.fup) - (improved.fup) == 0 -0.187
## (worse.t1) - (improved.t1) == 0 -0.355
## ((worse.fup) - (worse.t1)) - ((improved.fup) - (improved.t1)) == 0 0.119
## Pr(>|z|)
## (worse.fup) - (worse.t1) == 0 0.738
## (improved.fup) - (improved.t1) == 0 0.931
## (worse.fup) - (improved.fup) == 0 0.851
## (worse.t1) - (improved.t1) == 0 0.722
## ((worse.fup) - (worse.t1)) - ((improved.fup) - (improved.t1)) == 0 0.905
## (Adjusted p values reported -- none method)
We first do some quality controls: 1. Missing data: From one FND patients, no blood could be taken. 2. Hardy-Weinberg equilibrium: All data are in HWE
## alleles major.allele.freq HWE missing (%)
## rs1360780 C/T 74.5 0.710967 0
## rs1491850 T/C 61.8 0.769792 0
## rs1799732 G/I 87.3 1.000000 0
## rs1800532 G/T 53.9 0.574052 0
## rs2254298 G/A 91.2 1.000000 0
## rs3758653 T/C 78.4 0.676882 0
## rs3800373 A/C 74.5 0.710967 0
## rs4570625 G/T 77.5 0.418175 0
## rs53576 G/A 57.8 0.575946 0
## rs6265 C/T 81.4 1.000000 0
Next, we will do an association study, based on our FND
longitudinal data only. Previously, we compared FND patients with HC,
and we could show that there is a significant association between:
Age, gender, BDI, STAI were added as covariates of no-interest
## comments codominant dominant recessive log-additive
## rs1360780 - 0.75428 0.53589 0.84770 0.68267
## rs1491850 - 0.02777 0.04313 0.51719 0.29831
## rs1799732 - 0.85524 - - -
## rs1800532 - 0.95337 0.75950 0.87838 0.78135
## rs2254298 - 0.14790 - - -
## rs3758653 - 0.24532 0.80424 0.12098 0.76650
## rs3800373 - 0.75428 0.53589 0.84770 0.68267
## rs4570625 - 0.78621 0.87900 0.51335 0.98062
## rs53576 - 0.69473 0.54000 0.44065 0.40900
## rs6265 - 0.05685 0.03834 0.71111 0.11889
We can also look at the individual statistics of those genes found to be
associated with clinical course. rs1491850
(BDNF):
## [1] "Results rs1491850 T>C (BDNF):"
##
## SNP: rs1491850 adjusted by: age gender bdi stai
## 0 % 1 % OR lower upper p-value AIC
## Codominant
## T/T 9 56.2 11 31.4 1.00 0.02777 64.2
## C/T 3 18.8 20 57.1 8.99 1.41 57.24
## C/C 4 25.0 4 11.4 1.32 0.20 8.77
## Dominant
## T/T 9 56.2 11 31.4 1.00 0.04313 65.3
## C/T-C/C 7 43.8 24 68.6 4.21 0.99 17.98
## [1] "Results rs6265 G>A (BDNF):"
##
## SNP: rs6265 adjusted by: age gender bdi stai
## 0 % 1 % OR lower upper p-value AIC
## Codominant
## C/C 13 81.2 21 60.0 1.00 0.05685 65.6
## C/T 2 12.5 13 37.1 8.93 1.05 75.90
## T/T 1 6.2 1 2.9 0.95 0.04 23.66
## Dominant
## C/C 13 81.2 21 60.0 1.00 0.03834 65.1
## C/T-T/T 3 18.8 14 40.0 5.39 0.92 31.71
We find no significant association between the clinical course (group improved vs. worse/same) and changes in the CAR. SNP rs4570625 (TPH2) almost reaches significance. (Delta scores on AUC were calculated).
## comments codominant dominant recessive log-additive
## rs1360780 - 0.97010 0.88250 0.90152 0.94943
## rs1491850 - 0.23653 0.20076 0.12079 0.09829
## rs1799732 - 0.55401 - - -
## rs1800532 - 0.68362 0.44190 0.89146 0.68423
## rs2254298 - 0.46565 - - -
## rs3758653 - 0.26084 0.11848 0.98506 0.20912
## rs3800373 - 0.97010 0.88250 0.90152 0.94943
## rs4570625 - 0.05671 0.21378 0.07855 0.48689
## rs53576 - 0.99521 0.93220 0.98555 0.96241
## rs6265 - 0.27983 0.10944 0.49812 0.11996
For all analyses we take delta scores, which means that the statistics compares changes in brain volumes regarding clinical outcome.
## [1] "Genetic association with total insular volume:"
## comments codominant dominant recessive log-additive
## rs1360780 - 0.05280 0.22629 0.01604 0.05422
## rs1491850 - 0.25108 0.09909 0.62363 0.17655
## rs1799732 - 0.24798 - - -
## rs1800532 - 0.22532 0.09945 0.27713 0.09377
## rs2254298 - 0.22827 - - -
## rs3758653 - 0.43789 0.20814 0.88565 0.28591
## rs3800373 - 0.05280 0.22629 0.01604 0.05422
## rs4570625 - 0.03168 0.87319 0.00955 0.61596
## rs53576 - 0.08742 0.05257 0.09389 0.02653
## rs6265 - 0.38340 0.68632 0.16406 0.41271
## [1] "Genetic association with total hippocampal volume:"
## comments codominant dominant recessive log-additive
## rs1360780 - 0.87445 0.86793 0.60297 0.73216
## rs1491850 - 0.05754 0.01719 0.43926 0.04759
## rs1799732 - 0.39373 - - -
## rs1800532 - 0.97551 0.99465 0.83274 0.89856
## rs2254298 - 0.12637 - - -
## rs3758653 - 0.09066 0.83311 0.03263 0.33172
## rs3800373 - 0.87445 0.86793 0.60297 0.73216
## rs4570625 - 0.12602 0.12295 0.27001 0.25247
## rs53576 - 0.02466 0.03778 0.01818 0.00689
## rs6265 - 0.12398 0.33241 0.04292 0.13081
## [1] "Genetic association with total amygdalar volume:"
## comments codominant dominant recessive log-additive
## rs1360780 - 0.36427 0.38617 0.16977 0.21661
## rs1491850 - 0.10031 0.09379 0.66953 0.36605
## rs1799732 - 0.74658 - - -
## rs1800532 - 0.28096 0.10949 0.61741 0.19542
## rs2254298 - 0.36827 - - -
## rs3758653 - 0.88829 0.94164 0.63071 0.80845
## rs3800373 - 0.36427 0.38617 0.16977 0.21661
## rs4570625 - 0.08306 0.52551 0.04155 0.94168
## rs53576 - 0.02416 0.01024 0.07806 0.00712
## rs6265 - 0.39222 0.56347 0.17126 0.33875
## [1] "Genetic association with total Ncl. caudate volume:"
## comments codominant dominant recessive log-additive
## rs1360780 - 0.36488 0.76535 0.23753 0.80099
## rs1491850 - 0.05961 0.03983 0.89643 0.19344
## rs1799732 - 0.10315 - - -
## rs1800532 - 0.59712 0.99573 0.34218 0.58280
## rs2254298 - 0.82157 - - -
## rs3758653 - 0.46444 0.21351 0.64105 0.23767
## rs3800373 - 0.36488 0.76535 0.23753 0.80099
## rs4570625 - 0.00000 0.66270 0.00000 0.47375
## rs53576 - 0.05558 0.02230 0.12962 0.01831
## rs6265 - 0.53657 0.44880 0.59697 0.65830
## [1] "Genetic association with total putamen volume:"
## comments codominant dominant recessive log-additive
## rs1360780 - 0.05018 0.01548 0.20845 0.01653
## rs1491850 - 0.76014 0.46445 0.83950 0.55314
## rs1799732 - 0.28991 - - -
## rs1800532 - 0.06151 0.01817 0.54057 0.06732
## rs2254298 - 0.91599 - - -
## rs3758653 - 0.50489 0.38359 0.65451 0.59658
## rs3800373 - 0.05018 0.01548 0.20845 0.01653
## rs4570625 - 0.07406 0.16383 0.05085 0.06942
## rs53576 - 0.81423 0.71522 0.71727 0.96578
## rs6265 - 0.84410 0.55879 0.82601 0.57417
## [1] "Genetic association with total Ncl. accumbens volume:"
## comments codominant dominant recessive log-additive
## rs1360780 - 0.68373 0.66444 0.58570 0.91190
## rs1491850 - 0.83659 0.82082 0.69258 0.96517
## rs1799732 - 0.17142 - - -
## rs1800532 - 0.14089 0.69329 0.04969 0.16638
## rs2254298 - 0.76759 - - -
## rs3758653 - 0.84055 0.56459 0.95198 0.62626
## rs3800373 - 0.68373 0.66444 0.58570 0.91190
## rs4570625 - 0.00011 0.50079 0.00002 0.09588
## rs53576 - 0.02990 0.00792 0.45857 0.03167
## rs6265 - 0.96232 0.80961 0.95724 0.85629
rs4570625 (TPH2): There is only one homozygote for minor allele. For codominant model (heterozygote), risk allele carriers had smaller changes in brain volume in the Insula.
## [1] "Results rs1360780 C>T (FKBP5; recessive model):"
##
## SNP: rs1360780 adjusted by: group bdi stai
## n me se dif lower upper p-value AIC
## Recessive
## C/C-C/T 47 0.01042 0.06076 0.0000 0.01604 68.6
## T/T 4 -0.58105 0.35608 -0.5935 -1.059 -0.1282
## [1] "Results rs3800373 T>G (FKBP5; recessive model):"
##
## SNP: rs3800373 adjusted by: group bdi stai
## n me se dif lower upper p-value AIC
## Recessive
## A/A-C/A 47 0.01042 0.06076 0.0000 0.01604 68.6
## C/C 4 -0.58105 0.35608 -0.5935 -1.059 -0.1282
## [1] "Results rs4570625 G>T (TPH2; recessive/codominant model):"
##
## SNP: rs4570625 adjusted by: group bdi stai
## n me se dif lower upper p-value AIC
## Codominant
## G/G 29 -0.03219 0.09638 0.00000 0.031681 69.3
## G/T 21 -0.09230 0.06956 -0.06717 -0.3238 0.1894
## T/T 1 1.03710 0.00000 1.21382 0.2894 2.1382
## Recessive
## G/G-G/T 50 -0.05743 0.06266 0.00000 0.009551 67.6
## T/T 1 1.03710 0.00000 1.25065 0.3444 2.1569
rs53576 (OXTR): Risk allele carriers had a smaller change in volume in the hippocampus.
## [1] "Results rs1491850 G>T (BDNF; dominant model):"
##
## SNP: rs1491850 adjusted by: group bdi stai
## n me se dif lower upper p-value AIC
## Dominant
## T/T 20 -0.02800 0.03452 0.00000 0.01719 -53.1
## C/T-C/C 31 0.08645 0.02172 0.09899 0.02051 0.1775
## [1] "Results rs3758653 T>C (DRD4; dominant model):"
##
## SNP: rs3758653 adjusted by: group bdi stai
## n me se dif lower upper p-value AIC
## Recessive
## T/T-T/C 48 0.04967 0.02077 0.0000 0.03263 -51.8
## C/C 3 -0.08800 0.06391 -0.1859 -0.3512 -0.02052
## [1] "Results rs53576 G>T (OXTR; (co)dominant model):"
##
## SNP: rs53576 adjusted by: group bdi stai
## n me se dif lower upper p-value AIC
## Codominant
## G/G 18 0.08883 0.02250 0.00000 0.02466 -53.1
## G/A 23 0.04016 0.03632 -0.05876 -0.1417 0.024167
## A/A 10 -0.04026 0.03673 -0.15084 -0.2552 -0.046515
## Dominant
## G/G 18 0.08883 0.02250 0.00000 0.03778 -51.6
## G/A-A/A 33 0.01579 0.02810 -0.08639 -0.1656 -0.007228
## Recessive
## G/G-G/A 41 0.06153 0.02272 0.00000 0.01818 -53.0
## A/A 10 -0.04026 0.03673 -0.11778 -0.2120 -0.023531
rs53576 (OXTR): Minor allele carrier had a smaller difference in amygdalar volume (no difference = no change)
## [1] "Results rs4570625 G>T (TPH2; recessive model):"
##
## SNP: rs4570625 adjusted by: group bdi stai
## n me se dif lower upper p-value AIC
## Recessive
## G/G-G/T 50 0.02786 0.02265 0.0000 0.04155 -38.2
## T/T 1 0.37120 0.00000 0.3435 0.0224 0.6646
## [1] "Results rs53576 G>T (OXTR; (co)dominant model):"
##
## SNP: rs53576 adjusted by: group bdi stai
## n me se dif lower upper p-value AIC
## Codominant
## G/G 18 0.102283 0.04534 0.0000 0.02416 -40
## G/A 23 0.010478 0.03253 -0.1031 -0.1974 -0.008842
## A/A 10 -0.031780 0.02758 -0.1593 -0.2780 -0.040708
## Dominant
## G/G 18 0.102283 0.04534 0.0000 0.01024 -41
## G/A-A/A 33 -0.002327 0.02416 -0.1200 -0.2078 -0.032170
rs53576 (OXTR): Risk allele carriers had a smaller change in volume in the Ncl. Caudate
## [1] "Results rs1491850 G>T (BDNF; dominant model):"
##
## SNP: rs1491850 adjusted by: group bdi stai
## n me se dif lower upper p-value AIC
## Dominant
## T/T 20 -0.01050 0.02881 0.00000 0.03983 -46.8
## C/T-C/C 31 0.08043 0.02679 0.09006 0.006624 0.1735
## [1] "Results rs4570625 G>T (THP2; codominant and recessive model):"
##
## SNP: rs4570625 adjusted by: group bdi stai
## n me se dif lower upper p-value AIC
## Codominant
## G/G 29 0.04758 0.02021 0.00000 3.248e-06 -68.7
## G/T 21 0.00859 0.02505 -0.04392 -0.1102 0.02237
## T/T 1 0.72320 0.00000 0.65959 0.4208 0.89842
## Recessive
## G/G-G/T 50 0.03121 0.01583 0.00000 1.017e-06 -68.9
## T/T 1 0.72320 0.00000 0.68368 0.4459 0.92148
## [1] "Results rs53576 G>T (OXTR; dominant model):"
##
## SNP: rs53576 adjusted by: group bdi stai
## n me se dif lower upper p-value AIC
## Dominant
## G/G 18 0.10399 0.04194 0.00000 0.0223 -48
## G/A-A/A 33 0.01247 0.02059 -0.09896 -0.181 -0.01695
rs3800373 (FKBP5): Risk allele carriers had a smaller change in volume in the Putamen
## [1] "Results rs1360780 C>T (FKBP5; dominant model):"
##
## SNP: rs1360780 adjusted by: group bdi stai
## n me se dif lower upper p-value AIC
## Dominant
## C/C 29 0.08006 0.1083 0.0000 0.01548 100.4
## C/T-T/T 22 -0.30904 0.1430 -0.4346 -0.7733 -0.09585
## [1] "Results rs1800532 C>A (TPH1; dominant model):"
##
## SNP: rs1800532 adjusted by: group bdi stai
## n me se dif lower upper p-value AIC
## Dominant
## G/G 16 -0.33944 0.21052 0.0000 0.01817 100.8
## G/T-T/T 35 0.02726 0.08576 0.4629 0.09253 0.8332
## [1] "Results rs3800373 T>G (FKBP5; dominant model):"
##
## SNP: rs3800373 adjusted by: group bdi stai
## n me se dif lower upper p-value AIC
## Dominant
## A/A 29 0.08006 0.1083 0.0000 0.01548 100.4
## C/A-C/C 22 -0.30904 0.1430 -0.4346 -0.7733 -0.09585
rs3800373 (FKBP5): Risk allele carriers had a smaller change in volume in the Ncl. Accumbens
## [1] "Results rs1800532 C>A (TPH1; recessive model):"
##
## SNP: rs1800532 adjusted by: group bdi stai
## n me se dif lower upper p-value AIC
## Recessive
## G/G-G/T 39 0.003256 0.009456 0.0000 0.04969 -97.5
## T/T 12 0.058825 0.041212 0.0587 0.001625 0.1158
## [1] "Results rs4570625 G>T (DRD4; codominant and recessive model):"
##
## SNP: rs4570625 adjusted by: group bdi stai
## n me se dif lower upper p-value AIC
## Codominant
## G/G 29 0.006921 0.008831 0.000000 1.056e-04 -111.9
## G/T 21 0.012667 0.021627 0.004932 -0.03847 0.04833
## T/T 1 0.366200 0.000000 0.377125 0.22077 0.53349
## Recessive
## G/G-G/T 50 0.009334 0.010305 0.000000 1.723e-05 -113.9
## T/T 1 0.366200 0.000000 0.374421 0.22149 0.52735
## [1] "Results rs53576 G>T (OXTR; (co)dominant model):"
##
## SNP: rs53576 adjusted by: group bdi stai
## n me se dif lower upper p-value AIC
## Codominant
## G/G 18 0.059311 0.029491 0.00000 0.029903 -99.2
## G/A 23 -0.010200 0.009441 -0.07110 -0.1239 -0.01830
## A/A 10 -0.000010 0.015522 -0.06409 -0.1305 0.00234
## Dominant
## G/G 18 0.059311 0.029491 0.00000 0.007917 -101.1
## G/A-A/A 33 -0.007112 0.007996 -0.06900 -0.1177 -0.02030
For the last chapter, we will work on some predictive models, where we will use data from the fup as outcome variables, and try to predict them based on the variables at t1. As genotype did not change between sessions (or at least we hope our participants did not undergo gene-modifying therapies), we used sfmdrs at t1 as a covariate as well. There are not many findings, though. Here some of the most important findings:
1. Cortisol amplitude at T1 can predict symptom severity at fup 2. CARi/PACC/DBCC at T1 does not predict symptom severity at fup 3. Brain volume at T1 does not predict symptom severity at fup 4. TPH1, and OXTR polymorphism was associated with symptom severity at fup (This analyses are not really important, as genotype did not change between sessions). Also not always a reliable number of risk allele carriers. Most important findings are:THP2 (rs4570625): Heterozygous risk allele carriers associated with reduced symptom severity at fup (only one homozygote risk allele carrier though).
## [1] "Cortisol Amplitude at t1, predicts symptom severity at fup:"
##
## Call:
## glm(formula = sfmdrs[Data.AD$Session == "fup"] ~ Amplitude[Data.AD$Session ==
## "t1"] + age[Data.AD$Session == "t1"] + gender[Data.AD$Session ==
## "t1"] + DurationSymptoms[Data.AD$Session == "t1"] + bdi[Data.AD$Session ==
## "t1"] + stai1[Data.AD$Session == "t1"], data = Data.AD)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -16.163 -6.739 -1.053 4.655 29.892
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.14274 7.07474 0.868 0.3898
## Amplitude[Data.AD$Session == "t1"] 1.24785 0.57032 2.188 0.0338
## age[Data.AD$Session == "t1"] 0.06460 0.11548 0.559 0.5786
## gender[Data.AD$Session == "t1"]male 3.47514 3.30153 1.053 0.2980
## DurationSymptoms[Data.AD$Session == "t1"] -0.03072 0.02763 -1.112 0.2719
## bdi[Data.AD$Session == "t1"] 0.29872 0.17438 1.713 0.0934
## stai1[Data.AD$Session == "t1"] -0.27388 0.16257 -1.685 0.0988
##
## (Intercept)
## Amplitude[Data.AD$Session == "t1"] *
## age[Data.AD$Session == "t1"]
## gender[Data.AD$Session == "t1"]male
## DurationSymptoms[Data.AD$Session == "t1"]
## bdi[Data.AD$Session == "t1"] .
## stai1[Data.AD$Session == "t1"] .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 103.9779)
##
## Null deviance: 6036.5 on 52 degrees of freedom
## Residual deviance: 4783.0 on 46 degrees of freedom
## AIC: 405.04
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = sfmdrs[Data.AD$Session == "fup"] ~ Amplitude[Data.AD$Session ==
## "t1"] * group[Data.AD$Session == "fup"] + age[Data.AD$Session ==
## "t1"] + gender[Data.AD$Session == "t1"] + DurationSymptoms[Data.AD$Session ==
## "t1"] + bdi[Data.AD$Session == "t1"] + stai1[Data.AD$Session ==
## "t1"], data = Data.AD)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -21.821 -5.441 -1.147 4.739 26.548
##
## Coefficients:
## Estimate
## (Intercept) 15.95216
## Amplitude[Data.AD$Session == "t1"] -1.19067
## group[Data.AD$Session == "fup"]same/worse -7.91059
## age[Data.AD$Session == "t1"] 0.12209
## gender[Data.AD$Session == "t1"]male 2.15932
## DurationSymptoms[Data.AD$Session == "t1"] -0.01952
## bdi[Data.AD$Session == "t1"] 0.39161
## stai1[Data.AD$Session == "t1"] -0.42241
## Amplitude[Data.AD$Session == "t1"]:group[Data.AD$Session == "fup"]same/worse 2.74023
## Std. Error
## (Intercept) 10.81439
## Amplitude[Data.AD$Session == "t1"] 1.41639
## group[Data.AD$Session == "fup"]same/worse 8.44541
## age[Data.AD$Session == "t1"] 0.11214
## gender[Data.AD$Session == "t1"]male 3.17989
## DurationSymptoms[Data.AD$Session == "t1"] 0.02658
## bdi[Data.AD$Session == "t1"] 0.16938
## stai1[Data.AD$Session == "t1"] 0.16751
## Amplitude[Data.AD$Session == "t1"]:group[Data.AD$Session == "fup"]same/worse 1.55262
## t value
## (Intercept) 1.475
## Amplitude[Data.AD$Session == "t1"] -0.841
## group[Data.AD$Session == "fup"]same/worse -0.937
## age[Data.AD$Session == "t1"] 1.089
## gender[Data.AD$Session == "t1"]male 0.679
## DurationSymptoms[Data.AD$Session == "t1"] -0.734
## bdi[Data.AD$Session == "t1"] 2.312
## stai1[Data.AD$Session == "t1"] -2.522
## Amplitude[Data.AD$Session == "t1"]:group[Data.AD$Session == "fup"]same/worse 1.765
## Pr(>|t|)
## (Intercept) 0.1473
## Amplitude[Data.AD$Session == "t1"] 0.4051
## group[Data.AD$Session == "fup"]same/worse 0.3540
## age[Data.AD$Session == "t1"] 0.2822
## gender[Data.AD$Session == "t1"]male 0.5007
## DurationSymptoms[Data.AD$Session == "t1"] 0.4666
## bdi[Data.AD$Session == "t1"] 0.0255
## stai1[Data.AD$Session == "t1"] 0.0154
## Amplitude[Data.AD$Session == "t1"]:group[Data.AD$Session == "fup"]same/worse 0.0845
##
## (Intercept)
## Amplitude[Data.AD$Session == "t1"]
## group[Data.AD$Session == "fup"]same/worse
## age[Data.AD$Session == "t1"]
## gender[Data.AD$Session == "t1"]male
## DurationSymptoms[Data.AD$Session == "t1"]
## bdi[Data.AD$Session == "t1"] *
## stai1[Data.AD$Session == "t1"] *
## Amplitude[Data.AD$Session == "t1"]:group[Data.AD$Session == "fup"]same/worse .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 93.78134)
##
## Null deviance: 6036.5 on 52 degrees of freedom
## Residual deviance: 4126.4 on 44 degrees of freedom
## AIC: 401.22
##
## Number of Fisher Scoring iterations: 2
## [1] "Genetic association with symptom severity at fup:"
## comments codominant dominant recessive log-additive
## rs1360780 - 0.74460 0.82520 0.54101 0.95302
## rs1491850 - 0.45615 0.71291 0.36548 0.84222
## rs1799732 - 0.12766 - - -
## rs1800532 - 0.03957 0.39331 0.01058 0.04139
## rs2254298 - 0.55284 - - -
## rs3758653 - 0.59619 0.83795 0.38659 0.87464
## rs3800373 - 0.74460 0.82520 0.54101 0.95302
## rs4570625 - 0.00038 0.97192 0.00007 0.30853
## rs53576 - 0.18958 0.07489 0.30863 0.08062
## rs6265 - 0.80279 0.54259 0.64988 0.50627
## [1] "Results rs1800532 C>A (TPH1; dominant, recessive model):"
##
## SNP: rs1800532 adjusted by: group bdi stai sfmdrs
## n me se dif lower upper p-value AIC
## Dominant
## G/G 16 9.188 3.273 0.000 0.39331 380.9
## G/T-T/T 35 8.800 1.706 2.580 -3.287 8.446
## Recessive
## G/G-G/T 39 7.333 1.587 0.000 0.01058 374.3
## T/T 12 14.083 3.781 7.908 2.098 13.718
## [1] "Results rs4570625 G>T (THP2; codominant and recessive model):"
##
## SNP: rs4570625 adjusted by: group bdi stai
## n me se dif lower upper p-value AIC
## Codominant
## G/G 29 9.655 1.762 0.000 0.002728 383.2
## G/T 21 6.143 2.162 -4.064 -9.635 1.508
## T/T 1 46.000 0.000 32.034 11.963 52.105
## Recessive
## G/G-G/T 50 8.180 1.375 0.000 0.001634 383.5
## T/T 1 46.000 0.000 34.262 14.201 54.323
## R version 4.2.1 (2022-06-23)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] exactRankTests_0.8-35 SNPassoc_2.0-17 Gmisc_3.0.1
## [4] htmlTable_2.4.1 Rcpp_1.0.9 gridExtra_2.3
## [7] multcomp_1.4-20 TH.data_1.1-1 MASS_7.3-58.1
## [10] survival_3.4-0 mvtnorm_1.1-3 lawstat_3.5
## [13] car_3.1-1 carData_3.0-5 writexl_1.4.2
## [16] maditr_0.8.3 reshape2_1.4.4 rstatix_0.7.1
## [19] plotly_4.10.1 plyr_1.8.8 cowplot_1.1.1
## [22] gridGraphics_0.5-1 gg.gap_1.3 ggpubr_0.5.0
## [25] wesanderson_0.3.6 RColorBrewer_1.1-3 xlsx_0.6.5
## [28] readxl_1.4.1 forcats_0.5.2 stringr_1.5.0
## [31] dplyr_1.0.10 purrr_0.3.5 readr_2.1.3
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##
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